{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:23:13Z","timestamp":1750220593309,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,5,13]],"date-time":"2020-05-13T00:00:00Z","timestamp":1589328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["61972047"],"award-info":[{"award-number":["61972047"]}]},{"name":"the National Key Research and Development Program of China","award":["2018YFC0831500"],"award-info":[{"award-number":["2018YFC0831500"]}]},{"name":"the NSFC-General Technology Basic Research Joint Funds","award":["U1936220"],"award-info":[{"award-number":["U1936220"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,5,13]]},"DOI":"10.1145\/3399871.3399890","type":"proceedings-article","created":{"date-parts":[[2020,7,4]],"date-time":"2020-07-04T01:42:18Z","timestamp":1593826938000},"page":"27-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Call Attention to Stances"],"prefix":"10.1145","author":[{"given":"Zeng","family":"Lingyu","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"given":"Wu","family":"Bin","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"given":"Wang","family":"Bai","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2020,7,3]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Tim O'Reilly Xuan Weijian. What is Web2.0. Internet Weekly (40):38--40 (2005)  Tim O'Reilly Xuan Weijian. What is Web2.0. Internet Weekly (40):38--40 (2005)"},{"key":"e_1_3_2_1_2_1","unstructured":"Students Have 'Dismaying' Inability To Tell Fake News From Real Study Finds. www.npr.org\/sections\/thetwoway\/2016\/11\/23\/503129818\/study-fmds-students-havedisaying-inability-to-tell-fake-news-online-russia-election (2016)  Students Have 'Dismaying' Inability To Tell Fake News From Real Study Finds. www.npr.org\/sections\/thetwoway\/2016\/11\/23\/503129818\/study-fmds-students-havedisaying-inability-to-tell-fake-news-online-russia-election (2016)"},{"key":"e_1_3_2_1_3_1","volume-title":"International Joint Conference on Artificial Intelligence, 3818--3824","author":"Ma J","year":"2016","unstructured":"Ma J , Gao W , Mitra P , Detecting Rumors from Microblogs with Recurrent Neural Networks, The , International Joint Conference on Artificial Intelligence, 3818--3824 , ( 2016 ). Ma J, Gao W, Mitra P, et al. Detecting Rumors from Microblogs with Recurrent Neural Networks, The, International Joint Conference on Artificial Intelligence, 3818--3824, (2016)."},{"key":"e_1_3_2_1_4_1","first-page":"806","volume":"79","author":"Ruchansky N","year":"2017","unstructured":"Ruchansky N , Seo S , Liu Y. Csi : A hybrid deep model for fake news detection, Proceedings of the 2017 ACM on Conference on Information and Knowledge Management , 79 7-- 806 ( 2017 ). Ruchansky N, Seo S, Liu Y. Csi: A hybrid deep model for fake news detection, Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 797--806 (2017).","journal-title":"Knowledge Management"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/545"},{"key":"e_1_3_2_1_6_1","volume-title":"The 24th International Conference on World Wide Web, 1395--1405","author":"Zhao Z","year":"2015","unstructured":"Zhao Z , Resnick P , Mei Q. Enquiring minds : Early detection of rumors in social media from enquiry posts , The 24th International Conference on World Wide Web, 1395--1405 , ( 2015 ) Zhao Z, Resnick P, Mei Q. Enquiring minds: Early detection of rumors in social media from enquiry posts, The 24th International Conference on World Wide Web, 1395--1405, (2015)"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3184558.3188729"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2013.61"},{"key":"e_1_3_2_1_9_1","volume-title":"ACM International on Conference on Information and Knowledge Management, 1751--1754","author":"Ma J","year":"2015","unstructured":"Ma J , Gao W , Wei Z , Detect Rumors Using Time Series of Social Context Information on Mcroblogging Websites , ACM International on Conference on Information and Knowledge Management, 1751--1754 , ( 2015 ) Ma J, Gao W, Wei Z, et al. Detect Rumors Using Time Series of Social Context Information on Mcroblogging Websites, ACM International on Conference on Information and Knowledge Management, 1751--1754, (2015)"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1184"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Zhang L Liu B. Aspect and Entity Extraction for Opinion Mning Data Mining and Knowledge Discovery for Big Data 1--35 (2014)  Zhang L Liu B. Aspect and Entity Extraction for Opinion Mning Data Mining and Knowledge Discovery for Big Data 1--35 (2014)","DOI":"10.1007\/978-3-642-40837-3_1"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Liu B Zhang L. A survey of opinion mining and sentiment analysis Mning text data 415--463 (2012)  Liu B Zhang L. A survey of opinion mining and sentiment analysis Mning text data 415--463 (2012)","DOI":"10.1007\/978-1-4614-3223-4_13"},{"key":"e_1_3_2_1_13_1","first-page":"993","volume":"3","author":"Blei D M","unstructured":"Blei D M , Ng A Y , Jordan M I . Latent dirichlet al location, Journal of machine Learning research , 3 , 993 -- 1022 , (2003) Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation, Journal of machine Learning research, 3, 993--1022, (2003)","journal-title":"location, Journal of machine Learning research"},{"key":"e_1_3_2_1_14_1","first-page":"359","volume":"2","author":"Lv Pin","unstructured":"Lv Pin , Zhong Luo, Cai Dunbo et, al. Effective Mning Product Features from Chinese Review Based on CRF , Computer Engineering and Science , 2 , 359 -- 366 , (2014) Lv Pin, Zhong Luo, Cai Dunbo et, al. Effective Mning Product Features from Chinese Review Based on CRF, Computer Engineering and Science, 2, 359--366, (2014)","journal-title":"Science"},{"key":"e_1_3_2_1_15_1","volume-title":"Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 1195--1204","author":"Jin W","year":"2009","unstructured":"Jin W , Ho H H , Srihari R K . OpinionMiner : a novel machine learning system for web opinion mining and extraction , Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 1195--1204 , ( 2009 ) Jin W, Ho H H, Srihari R K. OpinionMiner: a novel machine learning system for web opinion mining and extraction, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 1195--1204, (2009)"},{"issue":"4","key":"e_1_3_2_1_16_1","first-page":"1","volume":"11","author":"Vosoughi S","unstructured":"Vosoughi S , Mohsenvand M , Roy D. Rumor Gauge : Predicting the Veracity of Rumors on Twitter. ACM Transactions on Knowledge Discovery from Data , 11 ( 4 ), 1 -- 36 , (2017) Vosoughi S, Mohsenvand M, Roy D. Rumor Gauge: Predicting the Veracity of Rumors on Twitter. ACM Transactions on Knowledge Discovery from Data, 11(4), 1--36, (2017)","journal-title":"Data"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S17-2081"},{"key":"e_1_3_2_1_18_1","volume-title":"Cross-Target Stance Classification with Self-Attention Networks, arXiv preprint","author":"Xu C","year":"2018","unstructured":"Xu C , Paris C , Nepal S , Cross-Target Stance Classification with Self-Attention Networks, arXiv preprint ( 2018 ). Xu C, Paris C, Nepal S, et al. Cross-Target Stance Classification with Self-Attention Networks, arXiv preprint (2018)."},{"volume-title":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2335--2338 (2017)","author":"Veyseh A P B","key":"e_1_3_2_1_19_1","unstructured":"Veyseh A P B , Ebrahimi J , Dou D , A temporal attentional model for rumor stance classification , Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2335--2338 (2017) Veyseh A P B, Ebrahimi J, Dou D, et al. A temporal attentional model for rumor stance classification, Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2335--2338 (2017)"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-68783-4_2"},{"key":"e_1_3_2_1_21_1","unstructured":"Zhang H Goodfellow I Metaxas D etal Self-attention generative adversarial networks arXiv preprint (2018).  Zhang H Goodfellow I Metaxas D et al. Self-attention generative adversarial networks arXiv preprint (2018)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Rush A M Chopra S Weston J. A neural attention model for abstractive sentence summarization arXiv preprint (2015)  Rush A M Chopra S Weston J. A neural attention model for abstractive sentence summarization arXiv preprint (2015)","DOI":"10.18653\/v1\/D15-1044"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSC.2019.00044"}],"event":{"name":"ASSE '20: 2020 Asia Service Sciences and Software Engineering Conference","sponsor":["Nanyang Technological University"],"location":"Nagoya Japan","acronym":"ASSE '20"},"container-title":["Proceedings of the 2020 Asia Service Sciences and Software Engineering Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3399871.3399890","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3399871.3399890","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:31:49Z","timestamp":1750195909000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3399871.3399890"}},"subtitle":["Detect Rumor with a Stance Attention Network"],"short-title":[],"issued":{"date-parts":[[2020,5,13]]},"references-count":23,"alternative-id":["10.1145\/3399871.3399890","10.1145\/3399871"],"URL":"https:\/\/doi.org\/10.1145\/3399871.3399890","relation":{},"subject":[],"published":{"date-parts":[[2020,5,13]]},"assertion":[{"value":"2020-07-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}